Wenhu Chen

Few-Shot NLG with Pre-Trained Language Model

2019 · arXiv (Cornell University)

Neural-based end-to-end approaches to natural language generation (NLG) from structured data or knowledge are data-hungry, making their adoption for real-world applications difficult with limited data. In this work, we propose the new task of \textit{few-shot …

HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data

2022 · arXiv (Cornell University)

A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities. Previous work in multiturn dialogue systems has primarily focused on either text or table …

Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models

2024

This paper investigates the capabilities of Large Language Models (LLMs) in the context of understanding their knowledge and uncertainty over questions.Specifically, we focus on addressing known-unknown questions, characterized by high uncertainty due to the absence …

BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent

2025 · arXiv (Cornell University)

Deep-Research agents, which integrate large language models (LLMs) with search tools, have shown success in improving the effectiveness of handling complex queries that require iterative search planning and reasoning over search results. Evaluations on current …